基于生成对抗网络的空间卫星低照度图像增强

针对空间低照度成像条件下卫星光学图像信息受损严重的问题,提出了一种基于生成对抗网络的空间卫星低照度图像增强方法,提高了图像的平均亮度及对比度,恢复图像细节信息,为图像识别等图像处理技术提供更高质量的数据信息.首先,设计了一种密集连接的生成器,加强了各特征提取阶段中的信息传递以及多层特征的融合,减少了特征信息的损耗,更好...

Low-light image enhancement of space satellites based on GAN

Aiming at the problem of serious information damage of satellite optical images under the low light imaging condition, we proposed a satellite low light image enhancement method based on GAN. The method can improve the average brightness and contrast of images, restore image details, and provide higher quality information for image processing techniques such as image recognition. Firstly, we designed a densely connected generator to strengthen the information propagation and fusion between each feature extraction phase, reduce the loss of feature, and better extract similar semantic information in normal light and low light images. Combining the idea of EnlightenGAN, the global local discriminator structure was introduced to enhance images more naturally. Under the condition of a small number of samples, unpaired training was used to the proposed method, and data enhancement methods such as random scaling and flipping of the input images were applied to improve the training effect and model performance. Finally, the proposed method was validated by simulation. The experimental results show that, under the condition of low illumination, the proposed method reduced NIQE by 1.034 and 0.699 compared with the LIME and EnlightenGAN. The proposed method can preserve more image details, realize higher overall and local brightness, higher contrast, and more natural effects of enhancement.